Uses real scientific case studies to show students the importance of applying sound statistical methods to evaluate findings and consider the statistical issues involved

Offers tutorials on using SAS, SPSS, MINITAB, and Microsoft Excel to perform the statistical calculations

Presents bootstrapping and Bayesian methods for estimation and hypothesis testing

Includes more than 1,000 exercises—many extracted from scientific journals—that promote students’ critical-thinking skills

Contains end-of-chapter summary materials that reinforce important points from the chapter and are useful study tools

Provides the data sets on the book's CRC Press web page

A solutions manual and figure slides are available upon qualifying course adoption.

Summary

Prepare Your Students for Statistical Work in the Real World

Statistics for Engineering and the Sciences, Sixth Edition is designed for a two-semester introductory course on statistics for students majoring in engineering or any of the physical sciences. This popular text continues to teach students the basic concepts of data description and statistical inference as well as the statistical methods necessary for real-world applications. Students will understand how to collect and analyze data and think critically about the results.

New to the Sixth Edition

Many new and updated exercises based on contemporary engineering and scientific-related studies and real data

More statistical software printouts and corresponding instructions for use that reflect the latest versions of the SAS, SPSS, and MINITAB software

Introduction of the case studies at the beginning of each chapter

Streamlined material on all basic sampling concepts, such as random sampling and sample survey designs, which gives students an earlier introduction to key sampling issues

New examples on comparing matched pairs versus independent samples, selecting the sample size for a designed experiment, and analyzing a two-factor experiment with quantitative factors

New section on using regression residuals to check the assumptions required in a simple linear regression analysis

The first several chapters of the book identify the objectives of statistics, explain how to describe data, and present the basic concepts of probability. The text then introduces the two methods for making inferences about population parameters: estimation with confidence intervals and hypothesis testing. The remaining chapters extend these concepts to cover other topics useful in analyzing engineering and scientific data, including the analysis of categorical data, regression analysis, model building, analysis of variance for designed experiments, nonparametric statistics, statistical quality control, and product and system reliability.

Table of Contents

Introduction STATISTICS IN ACTION DDT Contamination of Fish in the Tennessee River Statistics: The Science of Data Fundamental Elements of Statistics Types of Data Collecting Data: Sampling The Role of Statistics in Critical Thinking A Guide to Statistical Methods Presented in This Text STATISTICS IN ACTION REVISITED DDT Contamination of Fish in the Tennessee River—Identifying the Data Collection Method, Population, Sample, and Types of Data

Bivariate Probability Distributions and Sampling Distributions STATISTICS IN ACTION Availability of an Up/Down Maintained System Bivariate Probability Distributions for Discrete Random Variables Bivariate Probability Distributions for Continuous Random Variables The Expected Value of Functions of Two Random Variables Independence The Covariance and Correlation of Two Random VariablesProbability Distributions and Expected Values of Functions of Random Variables (Optional) Sampling DistributionsApproximating a Sampling Distribution by Monte Carlo SimulationThe Sampling Distributions of Means and Sums Normal Approximation to the Binomial Distribution Sampling Distributions Related to the Normal Distribution STATISTICS IN ACTION REVISITED Availability of an Up/Down Maintained System

Estimation Using Confidence Intervals STATISTICS IN ACTION Bursting Strength of PET Beverage Bottles Point Estimators and their PropertiesFinding Point Estimators: Classical Methods of Estimation Finding Interval Estimators: The Pivotal Method Estimation of a Population Mean Estimation of the Difference between Two Population Means: Independent Samples Estimation of the Difference between Two Population Means: Matched Pairs Estimation of a Population Proportion Estimation of the Difference between Two Population Proportions Estimation of a Population Variance Estimation of the Ratio of Two Population Variances Choosing the Sample Size Alternative Interval Estimation Methods: Bootstrapping and Bayesian Methods (Optional) STATISTICS IN ACTION REVISITED Bursting Strength of PET Beverage Bottles

Tests of Hypotheses STATISTICS IN ACTION Comparing Methods for Dissolving Drug Tablets—Dissolution Method Equivalence Testing The Relationship between Statistical Tests of Hypotheses and Confidence Intervals Elements and Properties of a Statistical Test Finding Statistical Tests: Classical Methods Choosing the Null and Alternative Hypotheses The Observed Significance Level for a TestTesting a Population Mean Testing the Difference between Two Population Means: Independent Samples Testing the Difference between Two Population Means: Matched Pairs Testing a Population Proportion Testing the Difference between Two Population Proportions Testing a Population Variance Testing the Ratio of Two Population Variances Alternative Testing Procedures: Bootstrapping and Bayesian Methods (Optional) STATISTICS IN ACTION REVISITED Comparing Methods for Dissolving Drug Tablets—Dissolution Method Equivalence Testing

Categorical Data Analysis STATISTICS IN ACTION The Case of the Ghoulish Transplant Tissue—Who Is Responsible for Paying Damages?Categorical Data and Multinomial Probabilities Estimating Category Probabilities in a One-Way Table Testing Category Probabilities in a One-Way Table Inferences about Category Probabilities in a Two-Way (Contingency) Table Contingency Tables with Fixed Marginal Totals Exact Tests for Independence in a Contingency Table Analysis (Optional) STATISTICS IN ACTION REVISITED The Case of the Ghoulish Transplant Tissue

Simple Linear Regression STATISTICS IN ACTION Can Dowsers Really Detect Water? Regression Models Model Assumptions Estimating β0 and β1: The Method of Least Squares Properties of the Least-Squares Estimators An Estimator of σ2Assessing the Utility of the Model: Making Inferences about the Slope The Coefficients of Correlation and Determination Using the Model for Estimation and Prediction Checking the Assumptions: Residual Analysis A Complete Example A Summary of the Steps to Follow in Simple Linear Regression STATISTICS IN ACTION REVISITED Can Dowsers Really Detect Water?

Multiple Regression Analysis STATISTICS IN ACTION Bid-Rigging in the Highway Construction Industry General Form of a Multiple Regression Model Model Assumptions Fitting the Model: The Method of Least Squares Computations Using Matrix Algebra: Estimating and Making Inferences about the Individual Parameters Assessing Overall Model Adequacy A Confidence Interval for and a Prediction Interval for a Future Value of y A First-Order Model with Quantitative Predictors An Interaction Model with Quantitative Predictors A Quadratic (Second-Order) Model with a Quantitative Predictor Regression Residuals and Outliers Some Pitfalls: Estimability, Multicollinearity, and Extrapolation A Summary of the Steps to Follow in a Multiple Regression Analysis STATISTICS IN ACTION REVISITED Building a Model for Road Construction Costs in a Sealed Bid Market

Model Building STATISTICS IN ACTION Deregulation of the Intrastate Trucking Industry Introduction: Why Model Building Is Important The Two Types of Independent Variables: Quantitative and Qualitative Models with a Single Quantitative Independent Variable Models with Two or More Quantitative Independent Variables Coding Quantitative Independent Variables (Optional) Models with One Qualitative Independent VariableModels with Both Quantitative and Qualitative Independent Variables Tests for Comparing Nested Models External Model Validation (Optional)Stepwise RegressionSTATISTICS IN ACTION REVISITED Deregulation in the Intrastate Trucking Industry

Nonparametric Statistics STATISTICS IN ACTION How Vulnerable Are New Hampshire Wells to Groundwater Contamination?Introduction: Distribution-Free Tests Testing for Location of a Single PopulationComparing Two Populations: Independent Random Samples Comparing Two Populations: Matched-Pairs Design Comparing Three or More Populations: Completely Randomized Design Comparing Three or More Populations: Randomized Block Design Nonparametric Regression STATISTICS IN ACTION REVISITED How Vulnerable Are New Hampshire Wells to Groundwater Contamination?

Statistical Process and Quality Control STATISTICS IN ACTION Testing Jet Fuel Additive for Safety Total Quality Management Variable Control Charts Control Chart for Means: x-Chart Control Chart for Process Variation: R-Chart Detecting Trends in a Control Chart: Runs AnalysisControl Chart for Percent Defectives: p-Chart Control Chart for the Number of Defects per Item: c-Chart Tolerance Limits Capability Analysis (Optional) Acceptance Sampling for Defectives Other Sampling Plans (Optional) Evolutionary Operations (Optional) STATISTICS IN ACTION REVISITED Testing Jet Fuel Additive for Safety

Product and System Reliability STATISTICS IN ACTION Modeling the Hazard Rate of Reinforced Concrete Bridge Deck Deterioration Introduction Failure Time Distributions Hazard Rates Life Testing: Censored Sampling Estimating the Parameters of an Exponential Failure Time Distribution Estimating the Parameters of a Weibull Failure Time Distribution System Reliability STATISTICS IN ACTION REVISITED Modeling the Hazard Rate of Reinforced Concrete Bridge Deck Deterioration

Author(s) Bio

William Mendenhall was a professor emeritus in the Statistics Department and the first chairman of the department at the University of Florida. Dr. Mendenhall published articles in top statistics journals and was a prolific author of statistics textbooks.

Terry L. Sincich is an associate professor in the Information Systems Decision Sciences Department at the University of South Florida, where he teaches introductory statistics at the undergraduate level and advanced statistics courses at the doctoral level. He has won numerous teaching awards, including the Kahn Teaching Award and Outstanding Teacher Award. Dr. Sincich is a member of the American Statistical Association and the Decision Sciences Institute. His research interests include applied statistical analysis and statistical modeling.

Reviews

"A salient feature of this book is the clarity with which many statistical concepts have been presented. A very nice blend of theory and applications. It contains a wealth of illustrative examples and problem sets. All the important concepts have been highlighted; real-life data has been extensively used throughout the book. Students will find it very appealing and useful on their way to learning the basic statistical concepts and tools."—Dharam V. Chopra, Wichita State University

"I like the problems because they are all based on engineering applications of probability and statistics. I especially like the problems at the end of chapters because students have to think more to solve them. I favor problems that require calculations because engineers are problem solvers." —Charles H. Reilly, University of Central Florida

"I think this text is one of the best I have seen when it comes down to real data sets. The authors successfully included small and large real data sets from various real-world problems in engineering, mathematical sciences, and natural sciences."—Edward J. Danial, Morgan State University

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